Designers of audio signal processing systems including auditory prostheses face the continuing challenge of attempting to eliminate feedback and noise from an input signal of interest. For example, a common complaint among users of auditory prosthesis such as hearing aids is their inability to understand speech in a noisy environment. In the past, hearing aid users were limited to listening-in-noise strategies such as adjusting the overall gain via volume control, adjusting the frequency response, or simply removing the hearing aid. More recent hearing aids have used noise reduction techniques based on, for example, the modification of the low frequency gain in response to noise. Typically, however, these strategies and techniques have been incapable of achieving a desired degree of noise reduction.
Many commercially available hearing aids are also subject to the distortion, ringing and squealing engendered by acoustical feedback. This feedback is caused by the return to the input microphone of a portion of the sound emitted by the acoustical hearing aid output transducer. Such acoustical feedback may propagate either through or around an earpiece used to support the transducer.
In addition to effectively reducing noise and feedback, a practical ear-level hearing aid design must accommodate the power, size and microphone placement limitations dictated by current commercial hearing aid designs. While powerful digital signal processing techniques are available, they require considerable space and power in the hearing aid hardware and processing time in the software. The miniature dimensions of hearing aids place relatively rigorous constraints on the space and power which may be devoted to noise and feedback suppression.
One approach to remedying the distortion precipitated by noise and feedback interference involves the use of adaptive filtering techniques. The frequency response of the adaptive filter can be made to self-adjust sufficiently rapidly to remove statistically "stationary" (i.e., slowly-changing) noise components from the input signal. Adaptive interference reduction circuitry operates to eliminate stationary noise across the entire frequency spectrum, with greater attenuation being accorded to the frequencies of high energy noise. However, environmental background noise tends to be concentrated in the lower frequencies, in most cases below 1,000 Hertz.
Similarly, undesirable feedback harmonics tend to build up in the 3,000 to 5,000 Hertz range where the gain in the feedback path of audio systems tends to be the largest. As the gain of the system is increased the distortion induced by feedback harmonics introduces a metallic tinge to the audible sound. Distortion is less pronounced at frequencies below 3,000 Hertz as a consequence of the relatively lower gain in the feedback path.
Although background noise and feedback energy are concentrated in specific spectral regions, adaptive noise filters generally operate over the entire bandwidth of the hearing aid. Adaptive noise filters typically calculate an estimate of noise by appropriately adjusting the weighting parameters of a digital filter in accordance with the Least Mean Square (LMS) algorithm, and then use the estimate to minimize noise. The relationship between the mean square error and the N weight values of the adaptive filter is quadratic. To minimize the mean square error, the weights are modified according to the negative gradient of an error surface obtained by plotting the mean square error against each of the N weights in N dimensions. Each weight is then updated by (i) computing an estimate of the gradient; (ii) scaling the estimate by a scaler adaptive learning constant, .mu.; and (iii) subtracting this quantity from the previous weight value.
This full-frequency mode of adjustment tends to skew the noise and feedback suppression capability of the filter towards the frequencies of higher signal energy, thereby minimizing the mean-square estimate of the energy through the adaptive filter. However, the set of parameters to which the adaptive filter converges when the full noise spectrum is evaluated results in less than desired attenuation over the frequency band of interest. Such "incomplete" convergence results in the noise and feedback suppression resources of the adaptive filter not being effectively concentrated over the spectral range of concern.
Accordingly, a need in the art exists for an adaptive filtering system wherein noise or feedback suppression capability is focused over a selected frequency band.